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Astro Lens

AI-Powered Space Research Assistant

Global Nominee – NASA Space Apps 2025
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Project Overview

Astro Lens was developed during the NASA International Space Apps Challenge 2025 and selected as a Global Nominee.

Built in just one day, Astro Lens is a cross-platform AI-powered research assistant that makes NASA’s space biology data more accessible and easier to understand. It simplifies complex scientific papers, enables semantic search, and allows users to ask natural-language questions to explore NASA’s research effortlessly.

Note: Astro Lens is currently a prototype/POC.

Achievements

Award Global Nominee
Hackathon Top 10% of 18K+ Teams
Duration Oct 2025 - Oct 2025

Key Features

  • AI-powered summarization of NASA research papers
  • Advanced Search - Search by title, summary, or keywords
  • Chat-style research assistant for natural questions
  • Cross-platform support (Web, Desktop, Mobile)
  • Paper Recommendations - Get direct links to relevant research papers
  • Modern, minimal, and responsive UI
  • Offline caching for faster paper access
  • AI-driven keyword extraction and related paper suggestions
  • Lightweight architecture optimized for performance
  • Built and presented during NASA Space Apps 2025 – Obour

Technologies Used

Flutter
Python
FastAPI
Gemini API
Cloudflare
Git & GitHub

Screenshots

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Technical Implementation

My technical contributions to this project include (but may not be limited to):

  • Built a responsive cross-platform web, desktop, and mobile interface using Flutter for seamless user interaction.
  • Engineered a high-performance FastAPI backend with caching, parsing, and AI-powered summarization for research queries.
  • Developed a RAG-based AI assistant using similarity-driven retrieval techniques.
  • Integrated Google Gemini 2.5 Flash Lite for advanced efficient natural language understanding and summarization of scientific texts.
  • Implemented real-time fetching of scientific papers using BeautifulSoup and the NCBI E-utilities API.
  • Utilized In-Memory Caching to optimize response times for frequently accessed research data.